Precise Calculation Research Articles

The first clinical validation of whole-genome screening on standard trophectoderm biopsies of preimplantation embryos

The first clinical validation of whole-genome screening on standard trophectoderm biopsies of preimplantation embryos

Four-dimensional dose evaluation for the liver stereotactic body radiotherapy with lipiodol retention after transcatheter arterial chemoembolization

PurposeTo evaluate the effect of lipiodol on the four-dimensional accumulated dose of liver stereotactic body radiotherapy (SBRT) after transcatheter arterial chemoembolization (TACE). Material and methodsTen patients who underwent liver SBRT with lipiodol retention after TACE were retrospectively analyzed. Free-breathing three-dimensional (3D) computed tomography (CT) and respiration-based four-dimensional (4D) CT scans were used. The tumor tissue and lipiodol region were delineated within the gross tumor volume (GTV). Volumetric modulated arc therapy plans were created using the X-ray Voxel Monte Carlo (XVMC), a fast MC algorithm. For 4D accumulated dose assessments, the dose was recalculated after copying the plan into each CT phase, and a deformable registration was used with the 3D CT as a reference. Additionally, dose variation due to tumor irradiation at the lipiodol location through motion was simulated by density override of planning target volume using tumor tissue density followed by dose recalculation. ResultsDose to the lipiodol region increased after density override with tumor density. In eight out of ten patients, 4D doses covering 98% of the GTV and tumor tissue increased compared to 3D doses. Notably, for both 3D and 4D cases, doses to the tumor tissue exceeded those of the GTV. Attention to GTV coverage in 3D planning for liver SBRT without considering lipiodol was deemed sufficient. The similar 19.2 Gy-liver volume for 3D and 4D allowed for liver evaluation using the 3D plan as a quick substitute. ConclusionsIn liver SBRT cases involving lipiodol after TACE, the XVMC planning system revealed higher 4D doses to tumor tissue compared to GTV, with 4D doses surpassing 3D. However, results were limited by XVMC accuracy, unable to reflect lipiodol's dose enhancement. For precise CT-based dose calculations involving lipiodol in the GTV, incorporating lipiodol's material information in the MC calculations is crucial.

A comprehensive thermodynamic modeling of the solubility of sugar alcohols in ionic liquids

Environmental regulations have recently drawn attention from fossil sources to green materials, among which sugar alcohols (SA) have particular importance due to their vast use in the nutrition, pharmaceutical, and chemical industries. Their properties and equilibrium conditions, including the extraction and solubility behavior utilizing novel solvents such as ionic liquids (ILs), are essential for developing further processes and their optimization. On account of this, the present study aims to develop a comprehensive thermodynamic approach based on simple and accurate tools such as activity coefficient models (ACMs) and equations of state (EoSs), which have not been hitherto addressed. To this end, eleven calculation schemes based on these two approaches were applied to the largest databank investigated thus far (13 SAs, 21 ILs, and 653 data points), and the performance of the models was analyzed. NRTL, free-volume Flory-Huggins (FVFH), and Redlich-Kister (RK) ACMs with different schemes, as well as athermal and ideal solution models, were evaluated and compared against well-known cubic EoSs such as Peng-Robinson (PR) and Valderrama-Patel-Teja (VPT). Benefitting from three adjustable parameters and a simple form, the three-parameter RK model yielded the best results with a 0.82 % (2.77 K) deviation in the entire databank. Moreover, employing temperature-dependent interaction parameters with FVFH ACM (3.55 K, 1.05 %), PR EoS (3.60 K, 1.07 %), and VPT EoS (3.66 K, 1.08 %) improved the accuracy of the calculations remarkably. The results of this contribution prove that thermodynamic models based on NRTL, FVFH, and RK ACMs are the best options for calculating solid–liquid conditions equilibrium of SA-IL systems with a simple and precise calculation procedure, and can therefore be employed for the simulation and optimization tools. This study investigates the most extensive SA-IL databank, a large number of models from different routes, and the nature of SA-IL pairs employing the obtained parameters.

Porous‐structured vermiculite/polybutylene adipate terephthalate composite films: The morphology, mechanical properties, and nonisothermal degradation kinetics

AbstractTo examine the impact of vermiculite (VMT) on the environmental and physical aspects of polybutylene terephthalate (PBAT), the thermal degradation behavior of VMT/polybutylene terephthalate polyadipate (VMT/PBAT) composite films was investigated by using the thermogravimetric analysis (TGA). To ensure precise calculation of the specific activation energy values of VMT/PBAT composite films in thermal degradation performance experiments, the Kissinger method (K‐method) and Flynn–Wall–Ozawa method (FWO method) were utilized. Additionally, the reaction mechanism was analyzed using the Coats–Redfern equation (CR equation). The introduction of VMT into the PBAT co‐polyester structure leads to an increase in the thermal degradation activation energy Ek of composite films.; the apparent activation energies were calculated by the Coats–Redfern method for different reaction mechanisms, and the interfacial diffusion reaction mechanism of the composite film was determined, with a thermal degradation reaction principle properties of g(α) = [1 − (1 − α)]2 and the number of reaction steps of 2.Highlights VMT/PBAT composite films prepared using a simple method. Analyzing nonisothermal degradation kinetics using K‐method and FWO method. Analyzing the degradation mechanisms using the Coats–Redfern equation. Addition of VMT enhances the thermal degradation properties of composite films. Addition of VMT reduces the mechanical properties of the composite film.

Molecular Insight into the Ternary Complex Formation between Ethambutol, Isoniazid and β‐Cyclodextrin

AbstractLong term intake of antituberculosis drugs will lead to severe adverse reactions that will ultimately deteriorate a patient's health and well‐being. With that, the ternary inclusion complex was explored, whereby β‐cyclodextrin (β‐CD) was chosen as the supramolecular host carrier to accommodate two kinds of first line antituberculosis guest drug molecules simultaneously, namely isoniazid (INH) and ethambutol (ETB) which would provide many therapeutic benefits. The inclusion complex of β‐CD/INH/ETB was prepared using solvent evaporation method. Fourier transform infrared spectroscopy (FT‐IR), proton nuclear magnetic resonance spectroscopy (1H‐NMR), and 2‐dimensional nuclear Overhauser effect spectroscopy (2D‐NOESY) NMR were used to investigate the functional groups and structure of the complex. Scanning electron microscopy (SEM) and X‐ray diffraction (XRD) were employed to study the surface morphology and crystallinity changes during complex formation. Thermogravimetric analysis (TGA) was performed to investigate the thermal properties of the complex. FT‐IR and both types of NMR results had revealed the successful penetration of both the drug molecules into the β‐CD cavity. SEM images and XRD spectra had shown a drastic change in surface structure and reduction in crystallinity during complex formation, which had indicated successful formation of a new compound. TGA had proven that the complex had significantly greater thermal stability as compared to the pure components. Molecular dynamics (MD) and molecular docking simulations were also performed to understand the molecular interaction between β‐CD, INH, and ETB. Through precise calculations of interaction and binding energies, β‐CD/INH/ETB was found to be a more stable inclusion complex compared to the binary β‐CD/INH and β‐CD/ETB complexes.

Covert Surveillance Video Transmission with QAM Constellations Modulation

This study explores the covert transmission of surveillance videos using QAM (quadrature amplitude modulation) constellations. By analyzing transmission strategies used in surveillance video systems, we adopt KL divergence as the constraint for covertness performance and mutual information to characterize transmission rates. Utilizing the Taylor series expansion method, we simplify the complex integral calculations of mutual information and KL divergence into summation operations. This simplification not only reduces computational complexity but also ensures precise calculations. Theoretical derivations establish the necessary conditions for the covert transmission of surveillance videos, optimizing modulation probabilities for constellation points. Experimental results and simulation validations demonstrate the superior performance of our proposed method in terms of both information transfer and precision.

Fast computation of highly oscillatory Bessel transforms

This study focuses on the efficient and precise computation of Bessel transforms, defined as ∫abf(x)Jν(ωx)dx. Exploiting the integral representation of Jν(ωx), these Bessel transformations are reformulated into the oscillatory integrals of Fourier-type. When a>0, these Fourier-type integrals are transformed through distinct complex integration paths for cases with b<+∞ and b=+∞. Subsequently, we approximate these integrals using the generalized Gauss–Laguerre rule and provide error estimates. This approach is further extended to situations where a=0 by partitioning the integral’s interval into two separate subintervals. Several numerical experiments are provided to demonstrate the efficiency and accuracy of the proposed algorithms.

Optimization of new fire department location using an improved GIS algorithm for firefighters travel time estimation

PurposeThe rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.Design/methodology/approachThis paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.FindingsThe results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.Practical implicationsThe results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.Originality/valueThe proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

Extended Higher Order Iterative Method for Nonlinear Equations and its Convergence Analysis in Banach Spaces

In this article, a novel higher order iterative method for solving nonlinear equations is developed. The new iterative method obtained from fifth order Newton-Özban method attains eighth order of convergence by adding a single step with only one additional function evaluation. The method is extended to Banach spaces and its local as well as semi-local convergence analysis is done under generalized continuity conditions. The existence and uniqueness results of solution are also provided along with radii of convergence balls. From the numerical experiments, it can be inferred that the proposed method is more accurate and effective in high precision computations than existing eighth order methods. The computation of error analysis and norm of functions demonstrate that proposed method takes a lead over the considered methods.

Prediction of drilling plug operation parameters based on incremental learning and CNN-LSTM

In order to solve the problems of low prediction accuracy and insufficient use of long time series data, and ensure the safety of drilling plug operation, a method for predicting drilling plug parameters based on incremental learning and the CNN-LSTM model is proposed. By making full use of the measured data of the drilling plug operation and using the sliding window processing method to process the data, then the CNN-LSTM deep learning model is established to predict the downhole parameters, and the reliability and generalization of the model are verified. The results show that the prediction accuracy of the machine learning model is the highest when the fixed width k = 4. According to the error results, it is concluded that CNN-LSTM model has the best processing and forecasting ability for drilling plug operation. By analyzing the prediction results of the whole well data, the validation accuracy of CNN-LSTM model reaches 99.5%, indicating that the model has high accuracy and generalization, and the model has fast calculation speed, providing a prediction method with high precision and fast calculation speed for drilling plug operation.

Mathematical modeling and analysis of the tailor rolled blank manufacturing process

A tailor rolled blank (TRB) is a plate of continuously varying thickness with the advantages of lightweight, high strength, and good surface quality. However, it is required that the roll gap can be adjusted online, and the plate and roll produce large elastic deformation in the TRB manufacturing process. These characteristics make the modeling of the force and deformation parameters more difficult. A new mathematical model of roll separating force in the TRB manufacturing process is established taking into account the feature of deformation in this research. The elastic deformation of the strip and the flattening deformation of the roll are considered in order to improve the prediction accuracy of the results. The roll separating forces of the entrance elastic compression region and the exit elastic recovery region are computed using the generalized Hooke's law. A new velocity field is proposed based on the deformation characteristics of the plastic region and velocity boundary conditions, and the power functionals of the internal deformation, shear, friction, and tension are calculated. The analytical models of force and deformation parameters combined the elastic and plastic deformation regions are obtained through iterative operation, which satisfies the convergence condition of the roll separating force and roll flattening. The roll separating force data obtained by the model in this research are compared with the actual measured values in the laboratory, and the deviation value is small, which proves the computational precision of the present model. Additionally, the variations of the entrance velocity and flow volume per second during the TRB manufacturing process are shown, and the influences of the tension and friction factor on the neutral angle and roll separating force are illustrated.

Energy-Efficient Spectral Analysis of ECGs on Resource Constrained IoT Devices.

Power spectral analysis (PSA) is one of the most popular and insightful methods, currently employed in several biomedical applications, aiming to identify and monitor various health related conditions. Among the most common applications of PSA is heart rate variability (HRV) analysis, which allows the extraction of further insights compared with conventional time-domain methods. Unfortunately, existing PSA approaches exhibit high computational complexity, hindering their execution on power-constrained embedded internet of things (IoT) devices. Such IoT devices are increasingly used for monitoring various conditions mainly by processing the input signals in the less complex time-domain. In this paper, a new low-complexity PSA system based on fast Gaussian gridding (FGG) is proposed, which can be used to calculate the Lomb-Scargle periodogram (LSP) of a non-uniformly spaced RR tachogram. The proposed approach is implemented on a popular ARM Cortex-M4 based embedded system, which is widely used in common wearables, and compared with conventional LSP-based approaches. Utilizing this experimental setup, a meticulous analysis is performed in terms of power, performance and quality under different operational settings, such as the total input/output samples, precision of computations, computer arithmetic (floating/fixed-point), and clock frequency. The experimental results show that the proposed FGG-based LSP approach, when specifically optimized for the targeted embedded device, outperforms existing approaches by up-to 92.99% and 91.70% in terms of energy consumption and total execution time respectively, with minimal accuracy loss.

ПРОГНОЗУВАННЯ ФІНАНСОВИХ ПОКАЗНИКІВ СУБ’ЄКТА ПІДПРИЄМНИЦЬКОЇ ДІЯЛЬНОСТІ: ВИБІР ОПТИМАЛЬНОГО МЕТОДУ

The use of economic and mathematical methods for solving financial problems is an integral part of effective management of enterprise resources and making sound economic decisions. This approach is becoming increasingly important in today’s world, as the dynamism of the financial environment and the variety of factors influencing the economy require complex analysis and precise calculations. The use of mathematical tools also allows to reduce the risks of making wrong decisions and improve the quality of forecasts. The article considers the necessity of forecasting financial indicators of a business entity as a process of predicting future financial results and the state of the enterprise for effective management and strategic decision-making, which gives an opportunity to ensure better resource planning, timely response to changes and reduce financial risks, contributing to the business sustainability and success. An analytical straight-line alignment of the dynamics of the amount of net revenue from sales was carried out on the basis of the financial statements of a real enterprise; a straight line equation characterising the amount of revenue from sales was derived and its forecast value for 2024 was calculated. The equation of the second-order parabola, which characterizes the trend in the value of sales revenue, was also derived and its forecast value for 2024 was found. For each method, the value of the standard square deviation of the actual levels of the dynamic series from the adjusted ones, i.e. the relative error of calculations, was calculated, and the optimal forecasting option was chosen – the one with the smaller relative error. It was found that the straight line more accurately reflects the main trend of a series of dynamics, which characterises the amount of revenue from sales of a business entity. On the basis of this conclusion and the obtained forecast data for 2024, the lower and upper limits of the projected value of sales revenue were calculated. It is concluded that forecasting of the financial performance of an enterprise allows to find optimal options for further development and planning of business activities and is a promising and relevant direction of predicting trends in the development of an enterprise, contributes to effective management and making sound strategic decisions.

A novel delayed exponent coupled chaotic map with countering dynamical degradation

&lt;abstract&gt; &lt;p&gt;While chaotic systems have found extensive applications across diverse scientific domains due to their inherent advantages, they often degrade into cyclic patterns when simulated on hardware with limited computational precision. This results in a pronounced decline in properties related to chaotic dynamics. To address this issue, we introduce the delayed exponent coupled chaotic map (DECCM). This model is designed to enhance the chaotic dynamics of the original map, especially at lower computational precisions. Additionally, DECCM can transform any proficient 1-dimensional seed map into an &lt;italic&gt;N&lt;/italic&gt;-dimensional chaotic map. Extensive simulation and performance tests attest to the robust chaotic characteristics of our approach. Furthermore, DECCM holds distinct advantages over premier algorithms, particularly in period analysis experiments. We also introduce various seed maps into DECCM to present 2D and 3D examples, ensuring their generalization through relevant performance evaluations.&lt;/p&gt; &lt;/abstract&gt;

Numerical analysis of the frequency-dependent Jiles-Atherton hysteresis model using the example of Terfenol-D material

&lt;p&gt;The Jiles-Atherton model has been widely used in describing the hysteretic property of a magnetic material or device. However, the calculation errors are not so easily discovered. With a complex expression, the frequency-dependent Jiles-Atherton model should be solved numerically with appropriate settings. This paper proposes an effective solving method for this model and describes some necessary analysis built on the numerical results. In the numerical method proposed in this manuscript, the anhysteretic magnetization was calculated by the secant method, and the trapezoidal rule was utilized to form the implicit function, which can be calculated by the fixed-point iteration. Compared to the other common methods, the proposed one has a friendly expression and fast computation speed. The Terfenol-D material was taken as an example for the numerical analysis. The feasible region was determined and the commonly used approximation that neglects the term of the magnetic field when calculating the magnetic induction intensity was tested. At last, the required number of sampling points per period was reached to guarantee high precision from analyzing its influence on the computation precision. The proposed numerical method is helpful for high-precision solutions of the frequency-dependent Jiles-Atherton model. The results from the numerical analysis can also help users avoid some incorrect calculations when employing this hysteresis model.&lt;/p&gt;

Comparative Review on Machine Learning-Based Predictive Modeling for Mechanical Characterization

The development of machine learning (ML) methods in the field of material science has provided new possibilities for predictive modeling, especially in the field of mechanical material evaluation. The study provides an in-depth investigation of the utilization of various machine learning methods in predicting of mechanical characteristics throughout a range of different materials. A range of supervised learning models, such as regression tree models, support vector machine models, and neural networks, have been used to examine and forecast significant mechanical properties, including strength, ductility, and toughness. The models completed training as well as validation processes employing broad datasets obtained from experimental mechanical tests, covering tensile, compression, and fatigue examinations. Major focus was given to the process of choosing features and optimization in order to boost the accuracy and dependability of the predictions. This approach not only simplifies the method of material development but also improves understanding of the complex links among material composition, methods of processing, and mechanical properties. The research further examines the barriers and potential outcomes of applying machine learning (ML) in material characterization. It stresses the possibility for further improvements in predicted precision and efficiency of computing. Support vector machines, supervised artificial neural network, regression trees are most popular ML technique used in conducting predictive modelling.

Parton Distribution Functions and Their Impact on Precision of the Current Theory Calculations

Parton Distribution Functions and Their Impact on Precision of the Current Theory Calculations

New physics in the muon magnetic moment?

The muon, a short-lived cousin of the electron, has provided a longstanding discrepancy between the standard model of particle physics and experimental measurements. This suggests that a not-yet-known particle or force perturbs the muon. The discovery of such “new physics” would have profound consequences on our understanding of Nature.In 2021, the attention of the world was drawn to this discrepancy when the announcement of the independent confirmation of the experiment by Fermilab [1] coincided with the publication of our ab-initio calculation (Nature [2]). Our result dramatically updated the theoretical prediction, bringing it significantly closer to the experimental value: it may be possible to explain the Fermilab measurement without any new physics, even with the latest Fermilab update [3]. Our result established a new standard of precision for such calculations that has yet to be challenged; with uncertainties comparable to the experimental measurement and the reference, data-driven computations.We are carrying out new simulations to reduce both the systematic and statistical uncertainties. Both improvements are required in order to match the precision of the final Fermilab measurement, to be obtained in the coming years. As such, these simulations will be critical to determine whether the muon's magnetic moment harbours new fundamental physics.

Assessing the efficacy of aluminum metal clusters Al13 and Al15 in mitigating NO2 and SO2 pollutants: a DFT investigation.

The present investigation delves into the adverse environmental impact of atmospheric pollutant gases, specifically nitrogen dioxide (NO2) and sulfur dioxide (SO2), which necessitates the identification and implementation of effective control measures. The central objective of this study is to explore the eradication of these pollutants through the utilization of aluminum Al13 and Al15 metal clusters, distinguished by their unique properties. The comprehensive evaluation of gas/cluster interactions is undertaken employing density functional theory (DFT). Geometric optimization calculations for all structures are executed using the ωB97XD functional and the Def2-svp basis set. To probe various interaction modalities, gas molecule distribution around the metal clusters is sampled using the bee colony algorithm. Frequency calculations employing identical model chemistry validate the precision of the optimization calculations. The quantum theory of atoms in molecules (QTAIM) and natural bond orbital (NBO) methodologies are applied for the analysis of intermolecular interactions. This research establishes the robust formation of van der Waals attractions between the investigated gas molecules, affirming aluminum metal clusters as viable candidates for the removal and control of these gases.

Guatemala soil organic carbon database (GTMSOC)

AbstractStudies on soil organic carbon stocks (SOCS) are increasingly relevant to developing efficient mitigation and adaptation strategies for climate change. Reliable information on soil organic carbon (SOC) content, bulk density (BD), and coarse fragments (CF) are required for precise SOCS calculation. The data quality of SOCS‐related variables is important to represent SOCS realistically across different levels of disturbance in the terrestrial ecosystems of Guatemala. The main objective is to develop Guatemala's first national SOC database to support studies of SOCS magnitudes and spatial trends. We identified and collected national sources of variables related to SOCS (SOC, BD, CF) across the country, mainly distributed in the central zone dominated by disturbed ecosystems and agricultural territories. We integrated 910 observations (soil samples and soil profiles) of SOC content (range 1.45–162 g·kg−1), 704 of BD (range 0.42–1.69 g·cm−3), and 8 of CFs (0%–21% weight). This new database represents the edaphic, climatic, and land use variability of Guatemalan territory. The database contains soil observations collected from 1965 to 2010. The year 2010 has the majority of soil observations (41%). SOCS‐related variables are standardized at 0–30 centimetres of depth using mass conservative splines, which are used to represent SOCS and soil depth relationships. We provide new information on SOCS across various ecological and environmental conditions to enable SOC monitoring systems to report reliable and accurate estimates. The new database is appealing for scientific and commercial purposes, such as representing Guatemalan soils in Earth system models or using soil information in the ecosystem services market (e.g., carbon markets). The new database is accessible to all users through the platform of the Environmental Data Initiative https://doi.org/10.6073/pasta/8dd15238c604c3ac75daf985548bd05c.